About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Fundamentals; Disruptive Innovation; The Demand for Data-Driven Insight; Developing a Business Strategy; Managing a Business Unit; Optimizing Business Processes; Developing Products and Services; Differentiating Products and Services; The Analytics Value Chain; Acquiring Data; Data Sources; Data Extraction; Data Cleansing; Data Structuring; Data Consolidation; Managing Data; Delivering Insight; Business Intelligence; Self-Service Discovery; Machine Learning; Overview of the Book
Chapter 2: A Short History of AnalyticsBefore the Data Warehouse; Birth of the Relational Database; Early Business Intelligence; Early Statistics and Machine Learning; Disruptive Analytics: Credit Scoring; The Data Warehouse Era; The Enterprise Data Warehouse Movement; Appliances and Columnar Datastores; MOLAP and ROLAP; Statistics, Machine Learning, and Data Mining; Disruptive Analytics: Fraud Detection; Key Trends Today; Digital Transformation of Business; The Data Tsunami; Summary; Chapter 3: Open Source Analytics; Open Source Fundamentals; Definitions; Project Governance
Open Source LicensesCode Management and Distribution; Donated Software; The Business of Open Source; Community Open Source; Commercial and Hybrid Open Source; Open Source Analytics; The R Project; Python; Scala; The Disruptive Power of Open Source; Chapter 4: The Hadoop Ecosystem; Hadoop and its Ecosystem; Apache Hadoop; Hadoop 2.0; Powered by Hadoop; Performance Improvements; The Economics of Hadoop; NoSQL Datastores; Analytics in Hadoop; Hadoop 1.0; Apache Hive; Apache Pig; Apache Mahout; Apache Giraph; Datameer; Hadoop 2.0; Apache Spark; Apache Impala; Apache Drill; Presto; Summary
Chapter 5: In-Memory AnalyticsIn-Memory Databases; Apache Spark; Capabilities; SQL Processing; Streaming Analytics; Machine Learning; Graph Analytics; Spark in Action; Apache Arrow; Alluxio; Apache Ignite; The New In-Memory Analytics; Chapter 6: Streaming Analytics; A Short History of Streaming Analytics; Fundamentals of Streaming Analytics; Stream Processing; Streaming Operations; Complex Event Processing; Streaming Machine Learning; Anomaly Detection; Streaming Data Sources; Apache ActiveMQ; Apache Kafka; Amazon Kinesis; RabbitMQ; Streaming Analytics Platforms; Apache Apex; Apache Flink
Apache SamzaApache Spark Streaming; Apache Storm; Streaming Analytics in Action; Streaming Economics; Chapter 7: Analytics in the Cloud; Cloud Computing Fundamentals; The Business Case for Cloud; Cloud Economics; Data Movement; Security; Analytics in the Public Cloud; Amazon Web Services; Storage Services; Compute Services; Hadoop Services; Database Services; Business Intelligence Services; Machine Learning Services; Marketplace; Microsoft Azure; Storage Services; Compute Services; Hadoop Services; Database Services; Business Intelligence Services; Machine Learning Services
Summary
Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. -- Edited summary from book
